Abstract: ECG contains very important clinical information about the cardiac activities of the heart. Often the ECG signal needs to be captured for a long period of time in order to identify abnormalities in certain situations. Such signal apart of a large volume often is characterised by low quality due to the noise and other influences. In order to extract features in the ECG signal with time-varying characteristics at first need to be preprocessed with the best parameters. Also, it is useful to identify specific parts of the long lasting signal which have certain abnormalities and to direct the practitioner to those parts of the signal. In this work we present a method based on wavelet transform, standard deviation and variable threshold which achieves 100% accuracy in identifying the ECG signal peaks and heartbeat as well as identifying the standard deviation, providing a quick reference to abnormalities.
Abstract: There have been significant improvements in automatic
voice recognition technology. However, existing systems still face difficulties,
particularly when used by non-native speakers with accents.
In this paper we address a problem of identifying the English accented
speech of speakers from different backgrounds. Once an accent is
identified the speech recognition software can utilise training set from
appropriate accent and therefore improve the efficiency and accuracy
of the speech recognition system. We introduced the Q factor, which
is defined by the sum of relationships between frequencies of the
formants. Four different accents were considered and experimented
for this research. A scoring method was introduced in order to
effectively analyse accents. The proposed concept indicates that the
accent could be identified by analysing their formants.